SCELoss-PyTorch

Official Repo: https://github.com/YisenWang/symmetric_cross_entropy_for_noisy_labels
Reproduce result for ICCV2019 paper "Symmetric Cross Entropy for Robust Learning with Noisy Labels"

Update

In the tensorflow version Official Repo, the model uses l2 weight decay of 0.01 on model.fc1, which will gives a better results. The code has been updated, now it should shows similar performance as in the paper.

How To Run

Arguments

• --loss: 'SCE', 'CE'
• --nr: 0.0 to 1.0 specify the nosie rate.
• --dataset_type: 'cifar10' or 'cifar100'
• --alpha: alpha for SCE
• --beta: beta for SCE
• --seed: random seed
• --version: For experiment notes

Example for 0.4 Symmetric noise rate with SCE loss

# CIFAR10
$ python3 -u train.py  --loss         SCE               \
	                     --dataset_type cifar10           \
                       --l2_reg       1e-2              \
                       --seed         123               \
                       --alpha        0.1               \
                       --beta         1.0               \
                       --version      SCE0.4_CIFAR10    \
                       --nr           0.4

# CIFAR100
$ python3 -u train.py  --lr           0.01              \
                       --loss         SCE               \
                       --dataset_type cifar100          \
                       --l2_reg       1e-2              \
                       --seed         123               \
                       --alpha        6.0               \
                       --beta         1.0               \
                       --version      SCE0.4_CIFAR100   \
                       --nr           0.4

Results on CIFAR10

Result of best Epoch

Loss	0.0	0.2	0.4	0.6	0.8
CE	92.68	84.70	72.77	54.14	31.23
SCE	92.05	89.96	84.65	73.77	36.28

Results on CIFAR100

Loss	0.0	0.2	0.4	0.6	0.8
CE	73.84	61.70	42.88	20.47	4.88
SCE	73.57	62.31	46.50	24.00	12.51